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We propose an efficient mixture classification technique, which uses electroencephalography (EEG) signals for establishing a communication channel for the physically challenged or immobilized people, by the usage of the brain signals. In order to identify the emotion expressions by an immobilized person, we introduce a novel approach for emotion recognition based on the generalized mixture distribution model. The main benefit of utilizing this model is that it is an asymmetric distribution, which helps to extract the EEG signals, which are either in symmetric or asymmetric form. The skew Gaussian distribution helps to identify the small duration EEG signal sample and helps toward better recognition of emotions in both clean and noisy EEG signals. The proposed method is particularly well suited for the high variability of the EEG signal allowing the emotions to be identified appropriately. The features of the brain signals are extracted by using cepstral coefficients. The extracted features are classified into different emotions using mixture classification techniques. In order to validate the model, six mentally impaired subjects are considered in the age group of 60-68, and an 8-channel EEG signal is utilized to collect the EEG signals under audio-visual stimuli. The basic emotions considered in this study include happy, sad, neutral, and boredom and an average emotion recognition accuracy of 89% is achieved.INDEX TERMS Brain-computer interaction (BCI), emotion recognition, affective computing, electroencephalography (EEG), Gaussian mixture, cepstral analysis.

The majority of research Study is moving towards cognitive computing, ubiquitous computing, internet of things (IoT) which focus on some of the real time applications like smart cities, smart agriculture, wearable smart devices. The objective of the research in this paper is to integrate the image processing strategies to the smart agriculture techniques to help the farmers to use the latest innovations of technology in order to resolve the issues of crops like infections or diseases to their crops which may be due to bugs or due to climatic conditions or may be due to soil consistency. As IoT is playing a crucial role in smart agriculture, the concept of infection recognition using object recognition the image processing strategy can help out the farmers greatly without making them to learn much about the technology and also helps them to sort out the issues with respect to crop. In this paper, an attempt of integrating kissan application with expert systems and image processing is made in order to help the farmers to have an immediate solution for the problem identified in a crop.

The rise in global temperatures, frequent natural disasters and rising sea levels, reducing Polar Regions have made the problem of understanding and predicting these global climate phenomena. Prediction is a matter of prime importance and they are run as computer simulations to predict climate variables such as temperature, precipitation, rainfall and etc. The agricultural country called India in which 60% of the people depending upon the agriculture. Rain fall prediction is the most important task for predicting early prediction of rainfall May helps to peasant's as well as for the people because most of the people in India can be depends upon the agriculture. The paper represents simple linear regression technique for the early prediction of rainfall. It can helps to farmers for taking appropriate decisions on crop yielding. As usually at the same time there may be a scope to analyze the occurrence of floods or droughts. The simple linear regression analysis methodology applied on the dataset collected over six years of Coonor in Nilagris district from Tamil Nadu state. The experiment and our simple linear regression methodology exploit the appropriate results for the rain fall.

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